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LanaGeis/README.md

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👋 Hi, I'm Lana

Data Analytics | Predictive Modeling | Finance → Data Science

I turn complex data into clear, actionable insights that support smarter business decisions.

💼 Background

  • 15+ years in finance and business operations
  • Strong foundation in analytical thinking and data-driven decision-making
  • Transitioning into data analytics and data science

🎓 Education

  • Master’s in Data Science (in progress)
  • Focus: Machine Learning, Statistics, Predictive Analytics

🚀 What I Do

  • 📊 Analyze data to uncover trends, risks, and opportunities
  • 🤖 Build models for prediction and anomaly detection
  • 📈 Design dashboards for business decision-making
  • 🧹 Clean and structure messy real-world data

🧠 Current Focus

  • Predictive analytics & anomaly detection
  • NLP and text-based modeling
  • Business-focused data storytelling

📂 Featured Projects

🔹 Employment Trend Analysis

AI & automation impact on jobs using BLS and O*NET data
→ Python analysis + Power BI dashboards

🔹 US Baby Names Exploration (1880–2019)

140-year trend analysis of cultural and generational shifts
→ Data cleaning + visualization in Python

🔹 Student Math Misunderstandings (Kaggle NLP)

NLP model to detect student misconceptions from open responses
→ TF-IDF, embeddings, classification models

🤝 Let’s Connect

📫 LinkedIn

💡 Value

I combine business experience + technical skills to not just analyze data—but translate it into decisions that drive efficiency, reduce risk, and support growth.

🛠️ Languages & Tools

Python R SQL pandas seaborn ggplot2

Power BI Tableau Excel Microsoft Office Excel Automation

Jupyter RStudio PyCharm GitHub Git

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  1. Employment-Trend-Analysis Employment-Trend-Analysis Public

    A milestone-based data science project exploring how automation and AI affect U.S. occupations and skills. Data collected from BLS, O*NET, and other sources, cleaned and merged into SQLite, with an…

    HTML

  2. US_baby_names_exploration US_baby_names_exploration Public

    A 140-year analysis of naming patterns, cultural shifts, and generational trends using the SSA dataset. Features data cleaning, Matplotlib visualizations, and exploratory analysis.

    Jupyter Notebook

  3. MAP-Student-Math-Misunderstandings_Kaggle MAP-Student-Math-Misunderstandings_Kaggle Public

    NLP + Machine Learning project identifying student math misconceptions using open-ended responses. Includes TF-IDF, embeddings, logistic regression, deep learning baselines, and full model evaluation.

    Jupyter Notebook